Device and Method for Training a Neuronal Network

1. A method for training a neural network, the neural network including a first layer, the first layer including a plurality of filters to provide a first layer output, the first layer output including a plurality of feature maps, the method comprising the following steps: receiving, from a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal; determining the first layer output based on the first layer input and a plurality of parameters of the first layer; determining a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps of the first layer output, the first layer loss value being obtained in an unsupervised fashion; and training the neural network, including adapting the parameters of the first layer, the adaption being based on the first layer loss value.

2. The method according to claim 1, the method further comprising the following steps: determining an output signal of the neural network depending on the first layer output, wherein the output signal characterizes a classification of the input signal; and determining a classification loss value based on the output signal; wherein the training includes adapting the parameters of the first layer based on the classification loss.

3. The method according to claim 1, wherein the first layer loss value characterizes a factoring of a distribution of the first layer output between the feature maps.

4. The method according to claim 1, wherein the first layer loss value is determined according to the following formula:, l = log ⁡ ( p ⁡ ( z ) ) + log ⁡ ( ❘ "\[LeftBracketingBar]" det ⁡ ( ∂ z ∂ i ) ❘ "\[RightBracketingBar]" ) , p ⁡ ( z ) = ∏ j = 1 C o ⁢ u ⁢ t A j · exp ⁡ ( b j ( ∑ z j 2 ) α ) , wherein i is the first layer input (i), Cout is a number of feature maps in first layer output (z), Aj, α and bj are predefined values and zj is a j-th feature map of the first layer output (z).

5. The method according to claim 4, wherein Ai and/or bi and/or α are adapted during training of the neural network.

6. The method according to claim 1, wherein the determining of the first layer output includes padding the first layer input such that a size of the first layer output matches a size of the first layer input along a height and/or width and/or depth.

7. The method as recited in claim 1, the method further comprising the following step: pruning the filters of the first layer.

8. A computer-implemented method for controlling an actuator using a neural network, the neural network including a first layer, first layer including a plurality of filters to provide a first layer output including a plurality of feature maps, the neural network having been trained by receiving, from a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal, determining the first layer output based on the first layer input and a plurality of parameters of the first layer, determining a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps of the first layer output, the first layer loss value being obtained in an unsupervised fashion, and training the neural network, including adapting the parameters of the first layer, the adaption being based on the first layer loss value, the method comprising: providing a second input signal to the trained neural network based on a sensor signal including data from a sensor; and providing an actuator control signal for controlling the actuator based on a second output signal of the trained neural network.

9. The method according to claim 8, wherein, based on the actuator control signal, the actuator controls an at least partially autonomous robot and/or a manufacturing machine and/or an access control system.

10. A non-transitory machine-readable storage medium on which is stored a computer program for training a neural network, the neural network including a first layer, the first layer including a plurality of filters to provide a first layer output, the first layer output including a plurality of feature maps, the computer program, when executed by a computer, causing the computer to perform the following steps: receiving, from a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal; determining the first layer output based on the first layer input and a plurality of parameters of the first layer; determining a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps of the first layer output, the first layer loss value being obtained in an unsupervised fashion; and training the neural network, including adapting the parameters of the first layer, the adaption being based on the first layer loss value.

11. A training system configured to train a neural network, the neural network including a first layer, the first layer including a plurality of filters to provide a first layer output, the first layer output including a plurality of feature maps, the control system configured to: receive, from a preceding layer, a first layer input in the first layer, wherein the first layer input is based on the input signal; determine the first layer output based on the first layer input and a plurality of parameters of the first layer; determine a first layer loss value based on the first layer output, wherein the first layer loss value characterizes a degree of dependency between the feature maps of the first layer output, the first layer loss value being obtained in an unsupervised fashion; and train the neural network, including adapting the parameters of the first layer, the adaption being based on the first layer loss value.